Minimal SO(10) GUT in 4D and its extension to 5D
Takeshi Fukuyama
TL;DR
This paper discusses the limitations of the minimal SUSY SO(10) GUT in 4D and explores how extending it to 5D can address these issues and improve the model's predictive power.
Contribution
It introduces a 5D extension of the minimal SUSY SO(10) GUT to overcome the conceptual and data-fitting challenges faced in 4D models.
Findings
5D extension improves model predictivity
Addresses conceptual problems of 4D GUTs
Facilitates broader data fittings
Abstract
The problems of renormalizable minimal SUSY SO(10) GUT in 4D are discussed. Its highly predictivity has been charged with many observations, which urges further progresses. We show why and how broad data fittings and conceptual problems drive us to 5D and how it improves the model.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.